The present invention has been specifically developed to tackle the problem of synthesizing, representing and transferring knowledge up to mutual understanding in a domain, field of endeavor or activities. The terms “domain”, “knowledge domain”, “field”, “line of business” and “corporate activities” are used synonymously. As Knowledge is “in the mind” of human beings, and not in documents, networks or machine, we do not address the topic of communication and information technologies or document technologies currently in use on the Internet or other computerized networks. We focus on the core knowledge carried by human actors in a domain, which knowledge is ultimately only accessible through the specialized language used by these actors in their day-to-day work activities. With regard to a Domain in a Company, diagramming representation methods have been used for years in learning, brainstorming, visual thinking, and problem solving by educators, engineers, psychologists, and consultants. Here is an overview of main diagramming methods.
A Concept Map is a “Box and arrow” diagram, in which ideas are represented as boxes and the relations between ideas as Arrows. In cases of Arguments mapping, boxes represent propositions to focus on the inferential relationships among propositions. A concept map is used for organizing and representing knowledge. An industry standard that implements formal rules for designing at least a subset of such diagrams is the Unified Modeling Language (UML).
A mind map, popularized by Tony Buzan, is a diagram used to represent words, ideas or other items linked to and arranged around a central key word or idea. The branches of a mindmap represent hierarchical tree structures denoting relationships with a central idea.
The Semantic Web, led by the World Wide Web Consortium, is dedicated to computers not to human beings; it could permit data to be shared and reused across computer applications by using common formats and including semantic metadata in web pages. But the Semantic Web will not permit knowledge transferring between human beings.
In the current state of the art, there are only informal methods for representing and mapping “what is in human mind”: existing method and tools cannot be formalized up to prove truth or falsity of “what they represent”. There is no way to bridge the gap between human reasoning and machine computation and data processing that is a limit to development of formal ontology and Semantic Web.
Our focus is on experience-based knowledge in a business domain embodied in people, called “The Experts”, who able to quickly apprehend a complex situation and come to a rapid and wise decision. There is no free access to expert's knowledge, as they don't (or can't) explain “How they reason”; sometime, they don't always want to reveal their data: “Information is power”! Heavyweight reporting, processes modeling, or rule-based management are not suitable solutions for managing “language assets”.
The present invention provides computational reasoning and methods for establishing and maintaining the content of formal glossaries, known as intelligent glossaries. Intelligent glossaries, are also described in U.S. Pat. No. 7,945,527. The present invention provides computational methods and systems for providing terminological precision, with regards to existing knowledge management capabilities, to model essential knowledge carried by human actors in a field of endeavor, line of business, activity or domain. The highly accurate methods and systems guarantee coherence and completeness of Semantic System of Intelligent Glossaries (SSIG) and enables the development of formal automatic interpretation of glossaries by a machine, for validating these glossaries by answering formal questions. The systems and methods as disclosed herein focus on the essential knowledge carried by human actors in a domain, whose knowledge is ultimately only accessible through the specialized language used by these humans in their day-to-day work activities. By way of example, legal documents may contain numerous idioms that the average person may not understand. The systems and methods described herein would tackle this problem by providing the user with a non-ambiguous definition for key terms, identifiers and symbols that are known within the legal field and providing them to the user in traditional jargon or language.
Several approaches to the concept of definition exist, based on mathematical, logical or data processing practices such as:                Providing a shortened but equivalent linguistic construction by mechanisms of abbreviation and acronym;        Using a meaningful mathematical or logical symbol equivalent to a term, which guarantees 100% identification of that term in any text in a natural language;        Enumerating the properties of an object or a concept to be defined;        Separating two complementary aspects of an object or a concept to be defined:                    1. “Black Box” perspective (inputs, outputs); and            2. “White Box” perspective (with exhaustive enumeration of the content of the Box).                        Studying linguistic relations like synonymy, antonymy, hyponymy and hyperonymy to build semantic nets of word meanings.        
Existing technologies including Description logics, Ontology, and Fuzzy logic are used to automate terminology. Currently, these technologies are not generally accessible and remain the province of computational linguistic specialists and researchers.
Description logics and ontology is the current state of the art. The purpose of ontology is to create a representation of the world, as well as the rules for combining representation elements to define ontological extensions. Such methods use first-order logic and set theory for knowledge modeling. For all practical purposes, description logics defines concepts and the relations between these concepts. In this approach, what is described is necessarily a set; set inclusion is the basic mechanism for inferring knowledge and ontological extensions; concepts are modeled as subsets of elements of the universe of discourse. An Ontology classifies concepts in accordance with the inclusion relation, which is well adapted to the definition of the vocabulary of a hierarchical environment (nomenclature). Ontology is a centralized data base for sharing knowledge; but there is no formal language for solving interpretation issues between two different ontology knowledge bases. As a result, Description logics is limited given the complexity of natural languages, which can refer to a variety of concepts and documents that are produced in a dynamic and decentralized way.
Fuzzy logic uses a translation mechanism of natural language sentences into a generalized language of numerical constraints. For example, in the sentence “almost all Swedes are tall”, the word almost means 80%, while the remainder of the sentence “all Swedes are tall” is a statement, which can be formalized as a constraint. This theory is ambitious; it tackles real problems and covers a vast array of concepts, but it is still under development.
Practices currently used to address terminology include lexicons, glossaries and dictionaries. A lexicon is a list of words, symbols or identifiers dedicated to a field of endeavor. A word, symbol or identifier listed in a lexicon is called a lexical element. A glossary is a document encompassing the definitions of the lexical elements included in a lexicon. Therefore, a glossary is not a dictionary, because it does not include the definitions of all the possible meanings of the same word; on the contrary, a glossary shows only the meaning agreed upon for a specific field.
Throughout the practice of generalizing and formalizing glossaries, the Essential Knowledge dilemma arises between: size of lexicon, on the one hand, and precision of words in natural language, on the other hand; in essence: If the concept of words with multiple meanings found in natural language is fully addressed, then much knowledge on a vast number of topics can be expressed. However, it requires a massive amount of documentation that remains vague and therefore not usable by a machine; if the word meaning is restricted and specified by using a formalized language, then a very precise, focused knowledge can be expressed. However, it is ultimately purely symbolic and machine readable; but it is only understood by experts in the field and in the formal language used; moreover, this no longer provides a useful global vision of the field.
Existing knowledge management methods and technologies do not address the Essential Knowledge dilemma and many questions arise: where to stop, given the combinatorial explosion of any terminology (to define a word, it is necessary to use other words)? What is really represented with each word? How is ambiguity eliminated in the meanings? The present invention was developed to help users solve these problems and the Essential Knowledge dilemma.
The present invention may apply Laws of Form (LoF), a mathematical theory created by George Spencer Brown, to lexical semantics; LoF is both a mental calculus (the Calculation of Distinctions), and a formal planar system (the Calculus of Indications). The LoF Calculation of Distinctions constrains the knowledge manager to conduct a comprehensive up-front Distinction-Based Reasoning (DBR), before writing a definition in a glossary; the LoF Calculus of Indications is used for computing formal meaningful values, i.e. the value of meaning of formal sentences, imbedding words and other lexical elements, which result from DBR analysis.
The present invention may formalize the glossary practice up to the capability of self-reference, i.e. the capability of formal self-definition, is reached; the present computer implemented method can then be re-used to formalize:    the syntax of alphabet, formulas and instructions authorized;    the application of a set of instructions to a formula;    the interpretation of the application of a set of instructions; and    the computation of the answer to a question, by interpretation of the application of a set of instructions to the question considered as a formula.
The present invention treats words as first class citizens—i.e. as numbers or other mathematical beings—which solves the previously described:    1. limit of ontology for describing non-numerical meaning, while being fully consistent with existing definitions of numbers, sets and first order logical languages;    2. basics of terminological precision, by producing intelligent glossaries, which are formal glossaries certified in accordance with the semantic interpretation of the process itself using the self-reference capability;    3. size of lexicon and precision of words by eliminating all computable meanings (compound words, opposite words, union of words, words hyponymy, . . . ).
The present invention allows automatic generation of a Minimal Lexicon from an Intelligent Glossary; such a lexicon is the smaller set of words for delimiting the field of endeavor of that glossary, which solves the Essential Knowledge dilemma. The present invention has been specifically developed to tackle the problem of synthesizing, representing and transferring knowledge up to mutual understanding in a domain, field of endeavor or activities.